In the face of compounding crises of social and economic inequality, many have turned to algorithmic decision-making to achieve greater fairness in society. As these efforts intensify, reasoning within the burgeoning field of "algorithmic fairness" increasingly shapes how fairness manifests in practice. This paper interrogates whether algorithmic fairness provides the appropriate conceptual and practical tools for enhancing social equality. I argue that the dominant, "formal" approach to algorithmic fairness is ill-equipped as a framework for pursuing equality, as its narrow frame of analysis generates restrictive approaches to reform. In light of these shortcomings, I propose an alternative: a "substantive" approach to algorithmic fairness that centers opposition to social hierarchies and provides a more expansive analysis of how to address inequality. This substantive approach enables more fruitful theorizing about the role of algorithms in combatting oppression. The distinction between formal and substantive algorithmic fairness is exemplified by each approach's responses to the "impossibility of fairness" (an incompatibility between mathematical definitions of algorithmic fairness). While the formal approach requires us to accept the "impossibility of fairness" as a harsh limit on efforts to enhance equality, the substantive approach allows us to escape the "impossibility of fairness" by suggesting reforms that are not subject to this false dilemma and that are better equipped to ameliorate conditions of social oppression.
翻译:面对社会和经济不平等的复杂危机,许多人转向了算法决策,以在社会上实现更大的公平。随着这些努力的加强,在“分析公平”这一新兴领域进行推理,“分析公平”这一新兴领域的推理越来越多地影响实践中的公平表现。本文询问算法公平是否提供了促进社会平等的适当概念和实践工具。我认为,对算法公平采取主导、“正式”的方法作为追求平等的框架设备不足,因为其狭隘的分析框架产生了限制性的改革方法。鉴于这些缺陷,我提议了一种替代方法:在“分析公平”方面采取“实质性”的方法,以反对社会等级制为中心,对如何处理不平等问题进行更加广泛的分析。这一实质性方法有助于更有成效地评估算法在打击压迫方面的作用。形式和实质性算法公平之间的区别体现在每种方法对“公平可能性”(算法公正性定义不相容)。 正式方法要求我们接受“公平可能性”的“合理性方法,以反对社会等级制度为基础,而不是以更严格的方式改进的进化方法来强化平等。